Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPathak, V.-
dc.contributor.authorAnanthanarayana, V.S.-
dc.identifier.citationICSESS 2012 - Proceedings of 2012 IEEE 3rd International Conference on Software Engineering and Service Science, 2012, Vol., , pp.757-760en_US
dc.description.abstractDue to the proliferation of high-speed internet access, more and more organizations are becoming vulnerable to potential cyber-attacks. An intrusion is defined as any set of actions that compromise the integrity, confidentiality or availability of a resource. Intrusion Detection System (IDS), as the main security defending technique, is widely used against malicious attacks. IDS system should be good enough to detect existing attacks as well as novel attacks at high speed. Thus to fulfil these requirements a new novel Multi-Threaded K-Means clustering approach has been used which has resulted in high detection rate and low false alarm rate. A subset of KDD99 Data set has been used as an input dataset for experiments. � 2012 IEEE.en_US
dc.titleA novel Multi-Threaded K-Means clustering approach for intrusion detectionen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
File Description SizeFormat 
7069.pdf1.11 MBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.